Harnessing Artificial Intelligence and Machine Learning Algorithms for Chronic Disease Management, Fall Prevention, and Predictive Healthcare Applications in Geriatric Care
DOI:
https://doi.org/10.62643/Keywords:
Geriatrics, Artificial Intelligence, Machine Learning, Chronic Illness, Fall Prevention, Predictive Healthcare, Elderly Population, Ensemble Model, Clinical Data, Wearable SensorsAbstract
Background Information: The management of chronic diseases, fall prevention, and proactive healthcare are essential for enhancing care for the ageing population. Artificial intelligence (AI) and machine learning (ML) provide sophisticated instruments for predictive modelling, facilitating prompt interventions and tailored treatment approaches in geriatric care.
Objectives: The objective of this work is to create predictive models utilising artificial intelligence and machine learning for the management of chronic diseases, fall detection, and preventative healthcare applications, hence improving care quality and patient outcomes in elderly populations.
Methods: Logistic Regression, Random Forest, and Convolutional Neural Network (CNN) models were trained using clinical and sensor data, both individually and in ensemble combinations, to forecast health risks.
Results: The ensemble model attained 92% accuracy, 90% precision, 89% recall, 90% F1-score, and 91% AUC-ROC, indicating superior predictive performance across all criteria.
Conclusion: In conclusion, ensemble-based AI models improve risk prediction for the aged, facilitating proactive treatments and enhancing healthcare outcomes for senior patients.
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